def get_face(self, img):
h, w, _ = img.shape
blob = cv2.dnn.blobFromImage(img, size=self.input_shape)
self.net.setInput(blob)
# output_names = ['loc', 'conf']
# loc, conf = self.net.forward(output_names)
loc, conf = self.net.forward(self.net.getUnconnectedOutLayersNames())
# Decode bboxes and landmarks
pb = PriorBox(input_shape=self.input_shape, output_shape=(w, h))
dets = pb.decode(np.squeeze(loc, axis=0), np.squeeze(conf, axis=0))
# Ignore low scores
idx = np.where(dets[:, -1] > self.conf_thresh)[0]
dets = dets[idx]
# NMS
if dets.shape[0] > 0:
dets = nms(dets, self.nms_thresh)
faces = dets[:self.keep_top_k, :]
# Draw boudning boxes and landmarks on the original image
boxs, face_rois = [], []
for i in range(faces.shape[0]):
# score = faces[i,-1]
box = (faces[i, :4]).astype(np.int32).tolist()
face_roi = img[box[1]:box[3], box[0]:box[2]]
landmark = faces[i, 4:14].reshape((5, 2))
if self.align:
face_roi = align_process(img, faces[i, :4], landmark,
(224, 224))
box.extend(landmark.astype(np.int32).ravel().tolist())
boxs.append(tuple(box))
face_rois.append(face_roi)
return boxs, face_rois
else:
return [], []
def detect(self, img):
h, w, _ = img.shape
blob = cv2.dnn.blobFromImage(img, size=self.input_shape)
self.net.setInput(blob)
# output_names = ['loc', 'conf']
# loc, conf = self.net.forward(output_names)
loc, conf = self.net.forward(self.net.getUnconnectedOutLayersNames())
# Decode bboxes and landmarks
pb = PriorBox(input_shape=self.input_shape, output_shape=(w, h))
dets = pb.decode(np.squeeze(loc, axis=0), np.squeeze(conf, axis=0))
# Ignore low scores
idx = np.where(dets[:, -1] > self.conf_thresh)[0]
dets = dets[idx]
# NMS
if dets.shape[0] > 0:
dets = nms(dets, self.nms_thresh)
faces = dets[:self.keep_top_k, :]
# Draw boudning boxes and landmarks on the original image
drawimg = img.copy()
for i in range(faces.shape[0]):
# score = faces[i,-1]
x1, y1, x2, y2 = (faces[i, :4]).astype(np.int32)
cv2.rectangle(drawimg, (x1, y1), (x2, y2), (0, 0, 255),
thickness=2)
face_roi = img[y1:y2, x1:x2]
# landmark = faces[i, 4:14].reshape((5, 2))
# landmark = landmark.astype(np.int32)
# for j in range(5):
# cv2.circle(drawimg, (landmark[j, 0], landmark[j, 1]), 2, (0, 255, 0), thickness=-1)
# # cv2.putText(drawimg, str(j), (landmark[j, 0], landmark[j, 1] + 12), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255))
blob = cv2.dnn.blobFromImage(face_roi,
scalefactor=1 / 255.0,
size=self.input_size,
swapRB=True)
self.pfld.setInput(blob)
_, landmarks = self.pfld.forward(['output', 'landmarks'])
pre_landmark = landmarks[0]
pre_landmark = pre_landmark.reshape(
-1, 2) * [face_roi.shape[1], face_roi.shape[0]]
# np.save('pfld_mtcnn/pfld_dnn_output.npy', pre_landmark)
for (x, y) in pre_landmark.astype(np.int32):
cv2.circle(drawimg, (x1 + x, y1 + y),
2, (0, 255, 0),
thickness=-1)
return drawimg
else:
print('No faces found.')
return img
def detect(self, img):
h, w, _ = img.shape
blob = cv2.dnn.blobFromImage(img, size=self.input_shape)
self.net.setInput(blob)
# output_names = ['loc', 'conf']
# loc, conf = self.net.forward(output_names)
loc, conf = self.net.forward(self.net.getUnconnectedOutLayersNames())
# Decode bboxes and landmarks
pb = PriorBox(input_shape=self.input_shape, output_shape=(w, h))
dets = pb.decode(np.squeeze(loc, axis=0), np.squeeze(conf, axis=0))
# Ignore low scores
idx = np.where(dets[:, -1] > self.conf_thresh)[0]
dets = dets[idx]
# NMS
if dets.shape[0] > 0:
dets = nms(dets, self.nms_thresh)
faces = dets[:self.keep_top_k, :]
# Draw boudning boxes and landmarks on the original image
drawimg, face_rois = img.copy(), []
for i in range(faces.shape[0]):
# score = faces[i,-1]
x1, y1, x2, y2 = (faces[i, :4]).astype(np.int32)
cv2.rectangle(drawimg, (x1, y1), (x2, y2), (0, 0, 255),
thickness=2)
face_roi = img[y1:y2, x1:x2]
landmark = faces[i, 4:14].reshape((5, 2))
if self.align:
face_roi = align_process(img, faces[i, :4], landmark,
(224, 224))
landmark = landmark.astype(np.int32)
for j in range(5):
cv2.circle(drawimg, (landmark[j, 0], landmark[j, 1]),
2, (0, 255, 0),
thickness=-1)
# cv2.putText(drawimg, str(j), (landmark[j, 0], landmark[j, 1] + 12), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 0, 255))
face_rois.append(face_roi)
return drawimg, face_rois
else:
print('No faces found.')
return img, []